Polarimetric Decomposition Analysis of ALOS PALSAR … · 2017. 5. 21. · Remote Sens. 2012, 4 2319 0 / 3 / 3 * HH HV HV VV 2 HV * HH VV 2 VV 2 2 2 HH S S S S S f S S f f f S f f

Remote Sens. 2012, 4, 2314-2328; doi:10.3390/rs4082314

Remote Sensing ISSN 2072-4292

www.mdpi.com/journal/remotesensing

Article

Polarimetric Decomposition Analysis of ALOS PALSAR

Observation Data before and after a Landslide Event

Chinatsu Yonezawa 1,*, Manabu Watanabe

2 and Genya Saito

3

1 Graduate School of Agricultural Science, Tohoku University, 1-1 Amamiya-machi, Tsutsumidori,

Aoba-ku, Sendai, Miyagi 981-8555, Japan 2

Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba 305-8505,

Japan; E-Mail: [email protected] 3

Tokyo Institute of Technology, Yokohama 226-8502, Japan; E-Mail: [email protected]

* Author to whom correspondence should be addressed; E-Mail: [email protected];

Tel.: +81-22-717-8661; Fax: +81-22-717-9542.

Received: 15 June 2012; in revised form: 30 July 2012 / Accepted: 30 July 2012 /

Published: 7 August 2012

Abstract: Radar scattering mechanisms over landslide areas were studied using

representative full polarimetric parameters: Freeman–Durden decomposition, and

eigenvalue–eigenvector decomposition. Full polarimetric ALOS (Advanced Land

Observation Satellite) PALSAR (Phased Array type L-band Synthetic Aperture

Radar) datasets were used to examine landslides caused by the 2008 Iwate-Miyagi

Nairiku Earthquake in northern Japan. The Freeman–Durden decomposition indicates that

areas affected by large-scale landslides show dominance of the surface scattering

component in both ascending and descending orbit data. The polarimetric parameters of

eigenvalue–eigenvector decomposition, such as entropy, anisotropy, and alpha angle, were

also computed over the landslide areas. Unsupervised classification based on the H-

plane

explicitly distinguishes landslide areas from others such as forest, water, and snow-covered

areas, but does not perform well for farmland. A landslide area is difficult to recognize

from a single-polarization image, whereas it is clearly extracted on the full polarimetric

data obtained after the earthquake. From these results, we conclude that 30-m resolution

full polarimetric data are more useful than 10-m resolution single-polarization PALSAR

data in classifying land coverage, and are better suited to detect landslide areas. Additional

information, such as pre-landslide imagery, is needed to distinguish landslide areas from

farmland or bare soil.

OPEN ACCESS

Remote Sens. 2012, 4

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Keywords: polarimetric SAR; scattering component disaster monitoring; earthquake;

landslide

1. Introduction

SAR (Synthetic Aperture Radar) is capable of observing the Earth’s surface in all weather conditions,

day and night. Full polarimetric SAR data provide a scattering matrix of a terrain’s surface. The

scattering matrix consists of magnitude and phase at four polarizations, HH, HV, VH, and VV, that

are sent and receive horizontally (H)- and vertically (V)-polarized waves by radar antenna [1].

Some methods have been suggested in order to obtain earth surface characteristics from full polarimetric

data. Eigenvector decompositions of a coherency or covariance matrix were proposed by Cloude and

Pottier [2]. An approach to the three-component scattering mechanism model was presented by

Freeman and Durden [3], and a four-component approach was presented by Yamaguchi et al. [4].

These and other decomposition methods were reviewed [5]. The usefulness of full polarimetric SAR

data for land cover classification was examined by Alberga et al. [6], and some of the techniques were

applied to disaster monitoring- and assessment applications. A landslide caused by the 1999 Chi-chi

earthquake in Taiwan was recognized using L-band airborne SAR data acquired after the disaster, and

the characteristics of radar scattering mechanism were examined. The extent of the landslide was

identified using data such as scattering entropy and anisotropy [7,8]. Other airborne L-band SAR data

with full polarimetry were analyzed by Watanabe et al. [9] to detect a landslide area in Japan using

three- and four-component decomposition techniques. It was shown that the landslide areas could

be effectively identified on the basis of the dominance of the surface scattering component of the

three-component scattering model.

The Iwate-Miyagi Nairiku earthquake (magnitude 7.2) struck an inland area of northern Honshu,

the main island of Japan, on 14 June 2008, causing large landslides within mountainous forest areas.

Several large landslides locally transformed forest to bare surface areas. Watanabe et al. [10] analyzed

full polarimetric data obtained from ALOS (Advanced Land Observation Satellite) data after the

earthquake, and showed that the landslide areas were well detected by combining the surface scattering

and backscattering coefficients in VH polarization (0

VH). ALOS carries an L-band (23.5-cm

wavelength) SAR, termed PALSAR (Phased Array type L-band Synthetic Aperture Radar). PALSAR

is the first space-borne SAR to have full polarizations mode. It also has three different polarimetric

observation modes: single polarization (mainly HH), dual polarization (mainly HH and HV), and full

polarization. Its range resolutions are 7–44, 14–88 and 24–89 m, respectively. In this paper, we used

PALSAR full polarimetric data, acquired in both ascending and descending orbits before and after the

earthquake-induced landslide. The pre- and post-landslide data used the same observation mode

(off-nadir angle, flight direction). Since relatively few global-scale observations have been carried out

by air-borne SAR, acquiring the data before the disaster with the same observation mode was hardly

achievable for a landslide area observation. We adopted two representative full polarimetric

decomposition models: a physically-based Freeman–Durden decomposition model, and a

mathematically-strict eigenvalue–eigenvector decomposition model. These models are applied not


2316

only to the landslide areas, but also to whole scenes, including forest, water surface and snow-covered

areas, and areas of farmland or bare soil. Furthermore, results from the full polarimetric analysis were

compared with those for single polarization. The comprehensive analysis results for landslide and

neighboring areas are presented to clarify the radar scattering characteristics over the landslide areas

by comparing other categories from the “actual” data, and to assist the identification of landslide areas

from the pre- and post-event data.

2. Study Area and Data

2.1. Study Area

The epicenter of the Iwate-Miyagi Nairiku earthquake is located in the southern, inland part of

Iwate Prefecture, at latitude 39°1.7′N and longitude 140°52.8′E (Figure 1).

Figure 1. Northern part of Honshu Island, Japan, with earthquake epicenter of the

Iwate-Miyagi Nairiku earthquake (cross) and Mt. Kurikoma (square). Blank squares show

observation areas of ALOS (Advanced Land Observation Satellite) PALSAR (Phased

Array type L-band Synthetic Aperture Radar) full polarimetric mode. “Asc.” orbit data was

obtained on 4 June 2009. Observation dates of “Desc.” orbits were 19 May 2006, 24

August 2008, 9 October 2008, and 24 November 2008. Shaded square corresponds with

the area shown in Figure 2.

Seismic intensity, measured by the Japan Meteorological Agency, was 6-plus in Oushu City, Iwate

Prefecture; and Kurihara City, Miyagi Prefecture. Seventeen people were killed and six were missing

following the earthquake; almost all of the victims (21 persons) were killed by sediment disasters

Asc.

HONSYU,

JAPAN

50 km

Iwate Pref.

Miyagi Pref.

Desc.

N

40°0′0″N

38°0′0″N

139°0′0″E 141°0′0″E


2317

caused by the earthquake [11]. The landslide areas were distributed in valleys adjacent to a

mountainous area. At 1,627 m, Mt. Kurikoma is the highest peak in the disaster zone. The top of the

mountain is covered by a snowcap from fall to spring.

Figure 2. ALOS AVNIR-2 (optical sensor, Advanced Visible and Near-infrared Radiometer

type 2) false-color composite (Bands 4, 3, 2 in red, green and blue, respectively) obtained

on 2 July 2008 after the 14 June 2008 earthquake. The white rectangle corresponds to the

area shown in Figure 3.

After the earthquake, many aerial photographs and much satellite data were obtained. Figure 2 shows

the affected areas observed by ALOS AVNIR-2 (optical sensor, Advanced Visible and Near-infrared

Radiometer type 2) on 2 July 2008. Figure 2 is a false-color composite image with landslides

recognizable from the bare surfaces characterized by blue-green color. We use this image as a reference


2318

for land coverage. The areas of earthquake damage are plotted on a damage status map [12,13]. The

damage status map was generated from visual interpretation of aerial photographs obtained on 15, 16

and 18 June with 0.4 m ground resolution by the Geospatial Information Authority of Japan (GSI); and

16 and 18 June by Asia Air Survey Co., Ltd. GSI also conducted a field survey on 17 and 18 June. The

affected regions were previously covered by forest and some farmland. Three major landslide event

areas were selected and analyzed during this time. One of the largest landslide areas is located at the

upstream end of the Aratozawa Dam reservoir. The affected area measured 900 × 1,300 m, and the

displaced soil mass was estimated as 70 million m3 by ground survey. A mudslide at Dozou-sawa, on

the eastern slope of Mt. Kurikoma, was analyzed, and its soil mass by slope failure was estimated as

1 million m3 by aerial photography [13]. The third landslide event area is Ubasume-gawa, where the

area is 400 × 300 m [14], and soil mass by slope failure is estimated as 5 million m3. Field

observations were carried out on 23 August 2008 at Aratozawa Dam reservoir. Several photos were

taken, and we observed that fallen trees were mixed in some parts of the landslide area.

2.2. PALSAR Data

PALSAR data of the disaster area were acquired via different observation modes. Many

observations have been carried out in single- and dual-polarization modes. Full polarimetric datasets,

including the epicenter before the earthquake, were acquired on 19 May 2006 in descending orbit.

Those after the earthquake were observed on 24 August 2008, 9 October 2008, and 24 November 2008

in descending orbit; and on 4 June 2009 in ascending orbit. The off-nadir angle of 4 June 2009 data

was 23.1°, and that of the other data was 21.5°. The observation areas are shown in Figure 1, and the

datasets are summarized in Table 1.

Table 1. Summary of Observation Mode for PALSAR (Phased Array type L-band Synthetic

Aperture Radar) Data.

Observation

Mode

Before Disaster After Disaster

Flight

Direction

Observation

Date

Off-nadir

Angle (°)

Flight

Direction

Observation

Date

Off-nadir

Angle (°)

Full polarimety

Desc. 19 May 2006 21.5 Desc. 24 August 2008 21.5

Desc. 9 October 2008 21.5

Desc. 24 November 2008 21.5

Asc. 4 June 2009 23.1

Single polarimetry Asc. 23 March 2008 34.3 Asc. 23 June 2008 34.3

3. Data Analysis

The polarimetric data were analyzed using PolSARpro software [15]. A Lee filter [16] was applied

with a window size of 3 × 3 pixels, to reduce speckle noise to Level 1.1 PALSAR data, and was

converted to the coherency matrix. We attempted a three-component polarimetric decomposition

model [3] for full polarimetric data analysis. This model describes the scattering contributions from

double-bounce, volume, and surface scattering by phase and intensity information in the polarimetric

SAR data, as indicated below.


2319

0

3/

3/

*

VVHV

*

HVHH

2

HV

*

VVHH

2

VV

222

HH

SSSS

fS

fffSS

fffS

fffS

v

vds

vds

vds

(1)

Here, Sij is the scattering coefficient, transmitted in the j polarization plane, and received in the

i polarization plane; and fv, fd, and fs are the contribution to the power in VV polarization from volume,

dihedral, and surface scattering. Parameters and are related to double-bounce and surface

scattering. Determining whether double-bounce or surface scattering is the dominant contribution by

using the sign of Re(SHHS*

VV) [17] enables us to identify the contribution of each scattering mechanism

solely from SAR polarization data, without any field data.

We also attempted eigenvalue analysis of the coherency matrix. An eigenvalue–eigenvector

decomposition was performed and scattering entropy H, polarimetric anisotropy A, and scattering

mechanism parameter

(alpha) were computed; unsupervised classification based on the H-

plane [2] was attempted. H-A-

parameters were independently defined. The elements of the scattering

matrix are defined as

VVVH

HVHH

SS

SSS (2)

The coherency matrix is defined as follows:

2**

*2*

**2

4)(2)(2

)(2))((

)(2))((

2

1

HVVVHHHVVVHHHV

VVHHHVVVHHVVHHVVHH

VVHHHVVVHHVVHHVVHH

SSSSSSS

SSSSSSSSS

SSSSSSSSS

T

†

3

3

2

1

3 ][

00

00

00

][ UU

(3)

Parameters 1, 2, and 3 are calculated eigenvalues of [T], conventionally ordered such that 0 ≤ λ3 ≤ λ2

≤ λ1.

Matrix [U3] is parameterized as:

321

321

332211

222211

321

3

cossincossincossin

cossincossincossin

coscoscos

][

jjj

jjj

eee

eeeU (4)

Parameter i is directly related to the angle of incidence and dielectric constant of the surface with i

ranging from 1 to 3. The i angles can be interpreted as orientation angles. γi and δi account for the

phase relations.

The appearance probability of each i contribution is given by


2320

3

1j

j

i

iP

(5)

The polarimetric scattering entropy is defined as follows:

3

1

3logi

ii PPH

,

10 H (6)

The randomness of the scattering process is measured by entropy.

The dominant scattering mechanism for each pixel is given by the

(alpha).

3

1i

iiP ,

900 (7)

Angle

is associated with the type of scattering mechanism, and yields direct information about the

scattering mechanism represented by each eigenvector. Consider a scattering matrix

b

aS

0

0 (8)

or rotated by . In case of a = b, which corresponds to surface scattering, the alpha parameter is 0. In

case of a = −b, which corresponds to dihedral scattering, the alpha, parameter is /2. In case of a >> b,

which corresponds to highly anisotropic or dipole scattering, the alpha parameter is /4. The other is in

between them.

Polarimetric anisotropy, A, is defined as follows:

32

32

A

,

10 A (9)

Anisotropy is used to characterize the scattering phenomenon.

One of the important characteristics for the eigenvalue–eigenvector decomposition is that the

parameters are rotation invariant, and the parameters are constant for rotation about the radar line of

sight. Table 2 shows a nine-zone segmentation scheme and represents scattering mechanisms on the

H-

plane.

Table 2. The H-

Plane Partitioned into Nine Zones [2,7].

Zone Entropy, H Alpha,

(°) Scattering Type

1 0.9–1.0 55–90 High Entropy Multiple Scattering

2 0.9–1.0 40–55 High Entropy Vegetation Scattering

3 0.9–1.0 0–40 High Entropy Surface Scattering

4 0.5–0.9 50–90 Medium Entropy Multiple Scattering

5 0.5–0.9 40–50 Medium Entropy Vegetation Scattering

6 0.5–0.9 0–40 Medium Entropy Surface Scattering

7 0–0.5 47.5–90 Low Entropy Multiple Scattering Events

8 0–0.5 42.5–47.5 Low Entropy Dipole Scattering

9 0–0.5 0–42.5 Low Entropy Surface Scattering


2321

4. Results and Discussion

4.1. Freeman–Durden Decomposition

Figure 3 shows ortho-rectified Freeman–Durden decomposition images with the double-bounce

shown in red, volume scattering in green, and surface scattering in blue for the data obtained before the

disaster on 19 May 2006 in descending orbit; after the disaster on 24 November 2008 in descending

orbit; and on 4 June 2009 in ascending orbit. Volume scattering is dominant within the extensive areas

of forest coverage.

Landslide areas upstream of Aratozawa Dam, Dozou-sawa, Ubusume-gawa, and Hiya-sawa on the

post-earthquake image show a larger surface scattering component than that on the pre-earthquake

image, and are consistent with the results derived by Watanabe et al. [10]. Surface scattering at

the landslide areas east of Mt. Kurikoma, Ubusume-gawa, and Dozou-sawa are recognized in the

post-earthquake images (see Figure 3(b,c,e–h)). Strong surface scattering upstream of Aratozawa Dam

also appeared on the post-earthquake images Figure 3(b,c), but this area was beyond the observation

area on 19 May 2006 (Figure 3(a)).

Strong surface scattering is found in the upper-left of Figure 3(a), in the area corresponding to the

upper slopes of Mt. Kurikoma. The AVNIR-2 image for 21 May 2006 shows that this area was

covered by snow, which may induce strong surface scattering. Some farmland in the middle of the

target area shows dominant surface scattering. Table 3 shows the contribution of the scattering

components derived from Freeman–Durden decomposition and double-bounce, volume, and surface

scattering within the landslide areas denoted “A” (Dozou-sawa) and “B” (Ubusume-gawa), shown by

the yellow rectangles in Figure 3(a–c). The images obtained of both areas on 19 May 2006, prior to the

earthquake, show that volume scattering accounts for more than 81% and surface scattering represents

14%. On the other hand, three datasets from the descending orbit obtained after the earthquake show

63–67% for the volume scattering and 29–33% for the surface scattering components at these two

landslide areas. After the earthquake, there was a shift of between 15 and 19%, from the volume

scattering to surface scattering component. There are three possible causes for stronger volume

scattering ratio in landslide areas.

(A) Volume scattering from fallen trees within the landslide areas. The presence of fallen trees

within the landslide areas was confirmed by aerial photographs and field observations.

(B) Very rough surfaces induce a 0hv component, leading to an erroneous volume scattering

component.

(C) Small contribution from forest areas, even if the areas enclosed by the yellow rectangles in

Figure 3 are occupied by the landslide areas.

Since the regions enclosed by the yellow rectangle are largely occupied by the landslide sites, and

the values change between the pre- and post-disaster measurements, we regarded the values in Table 4

as representative of landslide areas.


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Figure 3. Ortho-rectified Freeman–Durden decomposition images for (a) 19 May 2006,

(b) 24 November 2008, and (c) 4 June 2009. White rectangle area with Dozou-sawa

and Ubusume-gawa is expanded and shown in (d) 19 May 2006, (e) 24 August 2008,

(f) 9 October 2008, (g) 24 November 2008, and (h) 4 June 2009. The image shows

double-bounce scattering as red, volume scattering as green, and surface scattering as blue.


2323

The data obtained on 4 June 2009 in the ascending orbit shows volume scattering at 69% and

surface scattering at 25% in the Dozou-sawa area, and volume scattering at 79% and surface scattering

at 17% in the Ubusume-gawa area. The surface scattering ratio in the Ubusume-gawa area in the

ascending orbit image is smaller than that of the later image, obtained in descending orbit. The

discrepancy between the ascending and descending observations is not outstanding for H-

parameters

shown in Table 4 of the next section, and no significant difference was observed for the land surface of

the landslide areas. This is explained by the rotation invariance of H-

parameters, which are robust to

the difference in observation direction. The percentage of double-bounce scattering is 4–6% within

both landslide areas on every image.

Table 3. Percentage of dominant scattering mechanism in areas associated with landslides

following the 14 June 2008 Earthquake.

A (Dozou–sawa) B (Ubusume–gawa)

(%) Pd Pv Ps Pd Pv Ps

19 May 2006 4.5 81.7 13.8 5.0 81.0 14.1

24 August 2008 3.7 63.5 32.8 4.6 66.9 28.6

9 October 2008 4.9 63.2 31.9 5.2 66.0 28.8

24 November 2008 4.1 65.9 30.0 4.3 67.0 28.7

4 June 2009 6.2 69.2 24.6 4.8 78.7 16.5 Pd: Double bounce scattering, Pv: Volume scattering, Ps: Surface scattering.

4.2. H / A / Alpha Decomposition

Figure 4 shows an H-

classification map with range–azimuth projection. Entropy is lower over

the landslide area than it is over the surrounding forest areas. Entropy comes from the randomness of

the scattering process. High entropy is caused by the depolarizing effects of volume scatter from the

structural elements of trees, which produce greater disorder in scattering. Alpha is high for forest areas

and is low on bare surfaces, snow, and water surfaces. No significant differences were found in the

anisotropy image. These results are consistent with those obtained by Czuchlewski et al. [7].

Table 4 shows the entropy and alpha values, averaged over 4 × 16 windows from forest,

agricultural, landslide, water surface, and snowcapped areas, and the values are plotted in Figure 4(e).

For comparison, the same area sizes are picked from each category. The window size of 4 × 16 ensures

that pixels of the same category are included in a site. Since the width of the landslide areas is narrow,

the window size is almost decided by the size of the landslide areas. Each set of windows is selected

from every dataset, including the corresponding areas denoted on Figure 4 as “A” (forest), “B”

(agricultural), “C” (landslide), “D” (landslide), “E” (landslide), “F” (snow), and “G” (snow). Every

dataset shows a similar behavior, with relatively high entropy and alpha on the forest areas. The landslide

areas show lower entropy and alpha than the forest areas. Polarimetric entropy is more than 0.7 for forest

areas, compared with 0.52 to 0.63 for the landslide areas. The alpha angle for the forest areas is 47–52

and is 29–37 for the landslide areas. The agricultural areas show 0.59–0.70 for entropy and 30–40 for

alpha, and these values are similar to those of the landslide areas. This finding implies that additional

information, such as additional parameters [10] or pre-disaster land use classification, is required to

identify the landslide areas. Snow surface on 19 May 2006 shows lower entropy (0.14–0.28) and alpha


2324

(8–16) than the other land cover types. The entropy value of the water surface at Aratozawa Dam is

0.43–0.26, and is lower than that of the forest and the landslide areas. The relatively low entropy

values correspond with the areas where surface scattering is dominant. The effect of different

observation directions is evident from agricultural and water surface areas, and both the entropy and

alpha values of ascending data are larger than those of the descending data for these areas. Ridges on

agricultural fields and waves on the water surface may cause direction-dependent backscattering.

Figure 4. Unsupervised classification image using H-

segmentation with eight looks in

azimuth for (a) 19 May 2006, (b) 24 November 2008, and (c) 4 June 2009; (d) Partitioning

and color legend, with numbers referring to the region described in Table 4; (e) H-

plot

for each area shown in Table 4.

The H-

classification space has nine zones according to scattering mechanisms (Table 2,

Figure 4(d)). Large-scale landslide areas with bare surfaces are categorized as 6, 7, 8, and 9, whereas

they were categorized as 5, 6, and 9 in a previous study [7]. The extension to high alpha angle (zones 7

and 8) is observed in our data, which we take as evidence for the mixture of fallen trees in the landslide

area, as observed in the field. Forest areas are categorized as zones 4 and 5, whereas they were

categorized as 5 in a previous study [7]. The discrepancy may be explained by differing forest types.

Water surfaces show strong surface scattering areas. A large proportion of the water surface is

classified as category 9, surrounded by category-6 areas. This is consistent with the airborne SAR


2325

analysis [7], and explains that the water surface is smoother than the landslide surface on the scale of

L-band radar wavelength. Snowcapped slopes on a 19 May 2006 image were also categorized as zone

9, and behave similarly to water surfaces. A previous analysis of dry Alpine snow using L-band SAR

reported that surface backscattering was the major scattering mechanism at the snow–ground interface,

and that volume scattering and extinction were small [18].

Table 4. Average of Entropy and Alpha Values of 4 × 16 Windows.

Area Observation Date Entropy Alpha

Forest

(A*)

19 May 2006 0.741 50.936

24 August 2008 0.720 51.511

9 October 2008 0.767 50.184

24 November 2008 0.751 47.148

4 June 2009 0.731 47.794

Farmland

(B*)

19 May 2006 0.611 33.617

24 August 2008 0.595 35.439

9 October 2008 0.587 30.218

24 November 2008 0.630 34.434

4 June 2009 0.704 39.695

Landslide

(C*)

24 August 2008 0.636 36.947

9 October 2008 0.561 32.441

24 November 2008 0.581 35.824

4 June 2009 0.527 34.888

Landslide

(D*)

24 August 2008 0.573 32.176

9 October 2008 0.538 29.005

24 November 2008 0.601 35.962

4 June 2009 0.557 34.237

Water

(E*)

24 August 2008 0.290 16.086

9 October 2008 0.268 16.456

24 November 2008 0.262 14.871

4 June 2009 0.434 25.153

Snow (F*) 19 May 2006 0.145 8.337

Snow (G*) 19 May 2006 0.279 16.471 *Letters correspond to areas denoted in Figure 4.

4.3. Comparison with Single Polarization Observation

Figure 5(a) shows an image of Aratozawa Dam obtained by dividing an HH polarization intensity

image observed on 23 June 2008 by one observed on 23 March 2008. These data were acquired on

ascending orbit with a 34.3° incidence angle. Accumulations of black or white pixels indicate large

changes of radio-filled intensity, and also reveal changes in topographic features. The bright (white)

area denoted as “A” corresponds to sediments flowing into the reservoir. The low values (black)

denoted as “B” show occurrences of radar shadow by topographic features that were changed by the

earthquake. Formation of a cliff in this region by a landslide was confirmed by field survey after the

earthquake (Figure 5(b)). This radar shadow area is found on the polarimetric Freeman–Durden

decomposition image obtained from ascending orbit (Figure 3(c)). Descending images do not show


2326

this radar shadow, and the resultant images of eigenvalue–eigenvector decomposition do not include

the effect of the radar shadow.

Figure 5. (a) Single-polarization intensity image for Aratozawa Dam reservoir area

observed on 23 March 2008, divided by the image from 23 June 2008; (b) On-site

photograph taken around Area “B”.

Yonezawa et al. [19] suggested that PALSAR single polarization data are useful to detect changes

in terrain on landslides upstream of Aratozawa Dam and the temporal sequence of drawdown of the

dammed lake. Single polarization data detects changes to land features, but a pre-landslide image is

essential for the comparison. Spatial resolution of a full polarimetric image is coarser than that of a

single polarization image. However, full polarimetric data can assess land surface coverage from the

scattering mechanism. For example, the landslide at Ubusume-gawa is not recognized from visual

interpretation of single polarization data; however, it is clearly extracted on the full polarimetric data

obtained after the earthquake.

5. Conclusions

We employed Freeman–Durden decomposition and eigenvalue–eigenvector decomposition for

ALOS (Advanced Land Observation Satellite) PALSAR (Phased Array type L-band Synthetic

Aperture Radar) full polarimetric data. Data observed from different directions, ascending and

descending orbit with small incidence angles (21.5° and 23.5°) were examined in this study. Land

surface change from forest to bare soil due to landslides causes the scattering mechanism to change.

The surface scattering component is dominant in landslide areas. The volume scattering component is

strong in the forest areas. Agricultural, snow, and water surface areas also show a large surface

scattering component compared to forest areas. Unsupervised classification using the H-

plane makes

it possible to distinguish landslide areas from water and snow surface areas. Snow coverage and water


2327

areas generally show lower H and

values compared to landslide areas. Agricultural areas show

similar scattering characteristics to those of landslide areas. Areas of bare soil, that were present before

the earthquake, should show the same scattering properties as the landslide area. The difference in the

direction of geometric distortion associated with the differing observation direction has a slight effect

on the scattering ratio, H, and

values; however, the overall scattering properties are consistent with

each other. We conclude that landslide areas are well recognized by the post-earthquake full

polarimetry image, except for areas of farmland or bare soil. Classification accuracy is improved if the

land coverage before the disaster is known from optical sensors, aerial photographs, or other

references. Single polarization PALSAR images show landslide areas as topographic features that

differ before and after the earthquake. However, single polarization data is unsuitable for efficient

detection of landslide areas because of its difficulty in land cover classification.

Acknowledgments

ALOS data were provided under the agreement of a JAXA Research Announcement. We thank the

ESA/PolSARpro project team for providing the analysis software free of charge. This work is

supported by the Tohoku Construction Association and Japan Society for the Promotion of Science

(JSPS) Grants-in-Aid for Scientific Research, (C) 24510252.

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